Cone-beam computed tomography texture analysis can help differentiate odontogenic and non-odontogenic maxillary sinusitis
dc.contributor.author | Costa, Andre Luiz Ferreira | |
dc.contributor.author | Fardim, Karolina Aparecida Castilho [UNESP] | |
dc.contributor.author | Ribeiro, Isabela Teixeira [UNESP] | |
dc.contributor.author | Jardini, Maria Aparecida Neves [UNESP] | |
dc.contributor.author | Braz-Silva, Paulo Henrique | |
dc.contributor.author | Orhan, Kaan | |
dc.contributor.author | de Castro Lopes, Sérgio Lúcio Pereira [UNESP] | |
dc.contributor.institution | Cruzeiro do Sul University | |
dc.contributor.institution | Universidade Estadual Paulista (UNESP) | |
dc.contributor.institution | Universidade de São Paulo (USP) | |
dc.contributor.institution | Ankara University | |
dc.date.accessioned | 2023-07-29T16:10:48Z | |
dc.date.available | 2023-07-29T16:10:48Z | |
dc.date.issued | 2023-01-01 | |
dc.description.abstract | Purpose: This study aimed to assess texture analysis (TA) of cone-beam computed tomography (CBCT) images as a quantitative tool for the differential diagnosis of odontogenic and non-odontogenic maxillary sinusitis (OS and NOS, respectively). Materials and Methods: CBCT images of 40 patients diagnosed with OS (N=20) and NOS (N=20) were evaluated. The gray level co-occurrence (GLCM) matrix parameters, and gray level run length matrix texture (GLRLM) parameters were extracted using manually placed regions of interest on lesion images. Seven texture parameters were calculated using GLCM and 4 parameters using GLRLM. The Mann-Whitney test was used for comparisons between the groups, and the Levene test was performed to confirm the homogeneity of variance (α=5%). Results: The results showed statistically significant differences (P<0.05) between the OS and NOS patients regarding 3 TA parameters. NOS patients presented higher values for contrast, while OS patients presented higher values for correlation and inverse difference moment. Greater textural homogeneity was observed in the OS patients than in the NOS patients, with statistically significant differences in standard deviations between the groups for correlation, sum of squares, sum of entropy, and entropy. Conclusion: TA enabled quantitative differentiation between OS and NOS on CBCT images by using the parameters of contrast, correlation, and inverse difference moment. | en |
dc.description.affiliation | Postgraduate Program in Dentistry Cruzeiro do Sul University, SP | |
dc.description.affiliation | Department of Diagnosis and Surgery São José dos Campos School of Dentistry of the São Paulo State University, SP | |
dc.description.affiliation | Division of General Pathology School of Dentistry University of São Paulo, SP | |
dc.description.affiliation | Laboratory of Virology Institute of Tropical Medicine of São Paulo School of Medicine University of São Paulo, SP | |
dc.description.affiliation | Department of Dentomaxillofacial Radiology Faculty of Dentistry Ankara University | |
dc.description.affiliationUnesp | Department of Diagnosis and Surgery São José dos Campos School of Dentistry of the São Paulo State University, SP | |
dc.description.sponsorship | Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) | |
dc.format.extent | 43-51 | |
dc.identifier | http://dx.doi.org/10.5624/isd.20220166 | |
dc.identifier.citation | Imaging Science in Dentistry, v. 53, n. 1, p. 43-51, 2023. | |
dc.identifier.doi | 10.5624/isd.20220166 | |
dc.identifier.issn | 2233-7830 | |
dc.identifier.issn | 2233-7822 | |
dc.identifier.scopus | 2-s2.0-85152134648 | |
dc.identifier.uri | http://hdl.handle.net/11449/249847 | |
dc.language.iso | eng | |
dc.relation.ispartof | Imaging Science in Dentistry | |
dc.source | Scopus | |
dc.subject | Computer-Assisted | |
dc.subject | Cone-Beam Computed Tomography | |
dc.subject | Diagnosis | |
dc.subject | Diagnostic Imaging | |
dc.subject | Paranasal Sinuses | |
dc.title | Cone-beam computed tomography texture analysis can help differentiate odontogenic and non-odontogenic maxillary sinusitis | en |
dc.type | Artigo | |
unesp.author.orcid | 0000-0003-4856-5417[1] | |
unesp.author.orcid | 0000-0001-6001-4103[2] | |
unesp.author.orcid | 0000-0002-8748-468X[3] | |
unesp.author.orcid | 0000-0003-3229-8019[4] | |
unesp.author.orcid | 0000-0002-1842-9521 0000-0002-1842-9521[5] | |
unesp.author.orcid | 0000-0001-6768-0176[6] | |
unesp.author.orcid | 0000-0002-0882-5862[7] |